Save Episode. Episode Info Episode Info: I find it pretty amazing that even after all this time, and all the alerts and warnings, and all the bad things that have happened…the scammers are still able to trick people into opening email attachments. There are several ways for your computer to get infected with a virus or malware.
But do you know what the 1 most common method still is? Email attachments. As a computer tech, I talk to clients about this pretty regularly. It is fundamentally flawed. This is my policy on opening attachments: That sounds kind of counter-intuitve, right? You trust Bob completely. Bob is the godfather to your children. Bob once rescued you from a burning building. But then one day Bob opens an email attachment. So he just figured it was some kind of mistake, deleted the email and forgot about it.
That means he did not become suspicious and did not see any reason to investigate it further. That is exactly the response the hacker wants. But behind the scenes, not visible on the screen, the virus is now working hard to do whatever it was programmed to do. It might be installing a keylogger to track whatever is typed, such as social security numbers or credit card numbers or other malicious software. Probably the same email that Bob received, with the virus attached.
At this time the mathematician and philosopher Gottfried Wilhelm Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes Buchanan, The concept of intelligent machines for instructional use date back as early as , when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher. The Pressey Machine allowed user input and provided immediate feedback by recording their score on a counter. Pressey himself was influenced by Edward L.
Thorndike , a learning theorist and educational psychologist at the Columbia University Teacher College of the late 19th and early 20th centuries. Thorndike posited laws for maximizing learning. Thorndike's laws included the law of effect , the law of exercise , and the law of recency. Following later standards, Pressey's teaching and testing machine would not be considered intelligent as it was mechanically run and was based on one question and answer at a time,  but it set an early precedent for future projects.
By the s and s, new perspectives on learning were emerging.
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Burrhus Frederic "B. Rather, Skinner was a behaviourist who believed that learners should construct their answers and not rely on recognition. In the period following the second world war, mechanical binary systems gave way to binary based electronic machines. These machines were considered intelligent when compared to their mechanical counterparts as they had the capacity to make logical decisions. However, the study of defining and recognizing a machine intelligence was still in its infancy.
Alan Turing , a mathematician, logician and computer scientist, linked computing systems to thinking. One of his most notable papers outlined a hypothetical test to assess the intelligence of a machine which came to be known as the Turing test. Essentially, the test would have a person communicate with two other agents, a human and a computer asking questions to both recipients. The computer passes the test if it can respond in such a way that the human posing the questions cannot differentiate between the other human and the computer.
The Turing test has been used in its essence for more than two decades as a model for current ITS development. The main ideal for ITS systems is to effectively communicate. Turing's work as well as later projects by researchers such as Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of creating logical proofs and theorems. Their program, The Logic Theorist exhibited complex symbol manipulation and even generation of new information without direct human control and is considered by some to be the first AI program.
Such breakthroughs would inspire the new field of Artificial Intelligence officially named in by John McCarthy in at the Dartmouth Conference. The latter part of the s and s saw many new CAI Computer-Assisted instruction projects that built on advances in computer science. Although many supported this form of instruction, there was limited evidence supporting its effectiveness. PLATO, an educational terminal featuring displays, animations, and touch controls that could store and deliver large amounts of course material, was developed by Donald Bitzer in the University of Illinois in the early s.
At the same time that CAI was gaining interest, Jaime Carbonell suggested that computers could act as a teacher rather than just a tool Carbonell, A new perspective would emerge that focused on the use of computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems ITS.
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Further work began to showcase analogical reasoning and language processing. These changes with a focus on knowledge had big implications for how computers could be used in instruction. Towards the latter part of the s interest in CAI technologies began to wane. Developers and instructors were reacting negatively to the high cost of developing CAI programs, the inadequate provision for instructor training, and the lack of resources.
The microcomputer revolution in the late s and early s helped to revive CAI development and jumpstart development of ITS systems. In the transition from CAI to ICAI systems, the computer would have to distinguish not only between the correct and incorrect response but the type of incorrect response to adjust the type of instruction. Psychologists considered how a computer could solve problems and perform 'intelligent' activities.
An ITS programme would have to be able to represent, store and retrieve knowledge and even search its own database to derive its own new knowledge to respond to learner's questions. The ITTs were general purpose tutoring system builders and many institutions had positive feedback while using them. Different teachers created the ITAs and built up a large inventory of knowledge that was accessible by others through the Internet. Once an ITS was created, teachers could copy it and modify it for future use.
This system was efficient and flexible. However, Kinshuk and Patel believed that the ITS was not designed from an educational point of view and was not developed based on the actual needs of students and teachers Kinshuk and Patel, Modern day ITSs typically try to replicate the role of a teacher or a teaching assistant, and increasingly automate pedagogical functions such as problem generation, problem selection, and feedback generation. However, given a current shift towards blended learning models, recent work on ITSs has begun focusing on ways these systems can effectively leverage the complementary strengths of human-led instruction from a teacher  or peer,  when used in co-located classrooms or other social contexts.
The idea behind these projects was that since students learn best by constructing knowledge themselves, the programs would begin with leading questions for the students and would give out answers as a last resort. AutoTutor's students focused on answering questions about computer technology, Atlas's students focused on solving quantitative problems, and Why2's students focused on explaining physical systems qualitatively.
Graesser, VanLehn, and others,  Other similar tutoring systems such as Andes Gertner, Conati, and VanLehn,  tend to provide hints and immediate feedback for students when students have trouble answering the questions. They could guess their answers and have correct answers without deep understanding of the concepts.
Research was done with a small group of students using Atlas and Andes respectively. The results showed that students using Atlas made significant improvements compared with students who used Andes. Intelligent tutoring systems ITSs consist of four basic components based on a general consensus amongst researchers Nwana,;  Freedman, ;  Nkambou et al.
The domain model also known as the cognitive model or expert knowledge model is built on a theory of learning, such as the ACT-R theory which tries to take into account all the possible steps required to solve a problem. More specifically, this model "contains the concepts, rules, and problem-solving strategies of the domain to be learned.
It can fulfill several roles: as a source of expert knowledge, a standard for evaluating the student's performance or for detecting errors, etc. The student model can be thought of as an overlay on the domain model. It is considered as the core component of an ITS paying special attention to student's cognitive and affective states and their evolution as the learning process advances. As the student works step-by-step through their problem solving process, an ITS engages in a process called model tracing. Anytime the student model deviates from the domain model, the system identifies, or flags , that an error has occurred.
On the other hand, in constraint-based tutors the student model is represented as an overlay on the constraint set. The tutor model accepts information from the domain and student models and makes choices about tutoring strategies and actions. At any point in the problem-solving process the learner may request guidance on what to do next, relative to their current location in the model. In addition, the system recognizes when the learner has deviated from the production rules of the model and provides timely feedback for the learner, resulting in a shorter period of time to reach proficiency with the targeted skills.
Every time a student successfully applies a rule to a problem, the system updates a probability estimate that the student has learned the rule. Knowledge tracing tracks the learner's progress from problem to problem and builds a profile of strengths and weaknesses relative to the production rules. The cognitive tutoring system developed by John Anderson at Carnegie Mellon University presents information from knowledge tracing as a skillometer , a visual graph of the learner's success in each of the monitored skills related to solving algebra problems.
When a learner requests a hint, or an error is flagged, the knowledge tracing data and the skillometer are updated in real-time. The user interface component "integrates three types of information that are needed in carrying out a dialogue: knowledge about patterns of interpretation to understand a speaker and action to generate utterances within dialogues; domain knowledge needed for communicating content; and knowledge needed for communicating intent" Padayachee, , p.
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Nkambou et al. Nwana declares, "[I]t is almost a rarity to find two ITSs based on the same architecture [which] results from the experimental nature of the work in the area" p.
He further explains that differing tutoring philosophies emphasize different components of the learning process i. The architectural design of an ITS reflects this emphasis, and this leads to a variety of architectures, none of which, individually, can support all tutoring strategies Nwana, , as cited in Nkambou et al. Moreover, ITS projects may vary according to the relative level of intelligence of the components. As an example, a project highlighting intelligence in the domain model may generate solutions to complex and novel problems so that students can always have new problems to work on, but it might only have simple methods for teaching those problems, while a system that concentrates on multiple or novel ways of teaching a particular topic might find a less sophisticated representation of that content sufficient.
Apart from the discrepancy amongst ITS architectures each emphasizing different elements, the development of an ITS is much the same as any instructional design process. Corbett et al. The first stage known as needs assessment is common to any instructional design process, especially software development. The goal is to specify learning goals and to outline a general plan for the curriculum; it is imperative not to computerize traditional concepts but develop a new curriculum structure by defining the task in general and understanding learners' possible behaviours dealing with the task and to a lesser degree the tutor's behavior.
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In doing so, three crucial dimensions need to be dealt with: 1 the probability a student is able to solve problems; 2 the time it takes to reach this performance level and 3 the probability the student will actively use this knowledge in the future. Another important aspect that requires analysis is cost effectiveness of the interface. Moreover, teachers and student entry characteristics such as prior knowledge must be assessed since both groups are going to be system users.